Overview

Brought to you by YData

Dataset statistics

Number of variables41
Number of observations3410295
Missing cells4234040
Missing cells (%)3.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory998.5 MiB
Average record size in memory307.0 B

Variable types

Numeric23
Text6
Categorical6
DateTime3
Boolean3

Alerts

card_on_dark_web has constant value "False" Constant
customer_id is highly overall correlated with card_id and 1 other fieldsHigh correlation
birth_year is highly overall correlated with current_ageHigh correlation
credit_card_count is highly overall correlated with card_indexHigh correlation
current_age is highly overall correlated with birth_yearHigh correlation
per_capita_income is highly overall correlated with yearly_incomeHigh correlation
total_debt is highly overall correlated with per_capita_income and 1 other fieldsHigh correlation
yearly_income is highly overall correlated with per_capita_incomeHigh correlation
card_id is highly overall correlated with customer_id and 1 other fieldsHigh correlation
card_index is highly overall correlated with credit_card_countHigh correlation
credit_limit is highly overall correlated with per_capita_income and 2 other fieldsHigh correlation
transaction_id is highly overall correlated with card_id and 1 other fieldsHigh correlation
latitude is highly overall correlated with longitude and 1 other fieldsHigh correlation
longitude is highly overall correlated with merchant_zipHigh correlation
card_brand is highly overall correlated with card_number and 2 other fieldsHigh correlation
card_number is highly overall correlated with card_brand and 5 other fieldsHigh correlation
card_type is highly overall correlated with card_number and 2 other fieldsHigh correlation
merchant_mcc_code is highly overall correlated with transaction_typeHigh correlation
merchant_zip is highly overall correlated with card_brand and 7 other fieldsHigh correlation
transaction_error is highly overall correlated with transaction_typeHigh correlation
transaction_type is highly overall correlated with card_number and 5 other fieldsHigh correlation
cvv_code is highly overall correlated with card_brand and 4 other fieldsHigh correlation
fraud_detected is highly overall correlated with merchant_zipHigh correlation
gender is highly overall correlated with card_number and 2 other fieldsHigh correlation
has_chip is highly overall correlated with card_number and 3 other fieldsHigh correlation
merchant_id is highly overall correlated with transaction_typeHigh correlation
number_cards_issued is highly overall correlated with card_number and 2 other fieldsHigh correlation
has_chip is highly imbalanced (53.1%) Imbalance
fraud_detected is highly imbalanced (97.9%) Imbalance
transaction_error is highly imbalanced (58.0%) Imbalance
merchant_state has 428339 (12.6%) missing values Missing
merchant_zip has 449930 (13.2%) missing values Missing
transaction_error has 3355771 (98.4%) missing values Missing
transaction_id has unique values Unique
total_debt has 175422 (5.1%) zeros Zeros
card_index has 1264062 (37.1%) zeros Zeros

Reproduction

Analysis started2024-12-03 12:33:20.880232
Analysis finished2024-12-03 12:51:11.914159
Duration17 minutes and 51.03 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

High correlation 

Distinct1535
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1005.1929
Minimum0
Maximum1998
Zeros2301
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:12.310851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile98
Q1517
median1010
Q31483
95-th percentile1894
Maximum1998
Range1998
Interquartile range (IQR)966

Descriptive statistics

Standard deviation570.01167
Coefficient of variation (CV)0.56706693
Kurtosis-1.1518497
Mean1005.1929
Median Absolute Deviation (MAD)484
Skewness-0.021760796
Sum3.4280044 × 109
Variance324913.31
MonotonicityNot monotonic
2024-12-03T07:51:12.551367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1249 9915
 
0.3%
396 8768
 
0.3%
262 8754
 
0.3%
1101 8602
 
0.3%
1838 8478
 
0.2%
808 8195
 
0.2%
332 8044
 
0.2%
1150 7845
 
0.2%
972 7473
 
0.2%
1080 7450
 
0.2%
Other values (1525) 3326771
97.6%
ValueCountFrequency (%)
0 2301
0.1%
1 1122
 
< 0.1%
2 5588
0.2%
3 1746
 
0.1%
4 1926
 
0.1%
5 2298
0.1%
6 4267
0.1%
8 3412
0.1%
9 3519
0.1%
13 1069
 
< 0.1%
ValueCountFrequency (%)
1998 1457
< 0.1%
1997 2415
0.1%
1996 2299
0.1%
1995 2304
0.1%
1993 1036
 
< 0.1%
1992 3100
0.1%
1990 2841
0.1%
1989 2077
0.1%
1987 3210
0.1%
1986 1251
 
< 0.1%
Distinct1535
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:13.109900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length64
Median length57
Mean length40.954623
Min length29

Characters and Unicode

Total characters139667346
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row295 West Avenue, Bienville, LA 71008
2nd row295 West Avenue, Bienville, LA 71008
3rd row295 West Avenue, Bienville, LA 71008
4th row295 West Avenue, Bienville, LA 71008
5th row295 West Avenue, Bienville, LA 71008
ValueCountFrequency (%)
unit 933847
 
3.9%
street 751660
 
3.2%
drive 719998
 
3.0%
avenue 679579
 
2.9%
boulevard 662549
 
2.8%
lane 596509
 
2.5%
ca 420395
 
1.8%
tx 285644
 
1.2%
ny 233086
 
1.0%
fl 223048
 
0.9%
Other values (3885) 18153746
76.7%
2024-12-03T07:51:13.671188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20249766
 
14.5%
e 9909735
 
7.1%
, 7754437
 
5.6%
n 5898204
 
4.2%
t 5700750
 
4.1%
r 5260304
 
3.8%
a 5240569
 
3.8%
i 5164333
 
3.7%
o 4182914
 
3.0%
l 3994458
 
2.9%
Other values (54) 66311876
47.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 139667346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
20249766
 
14.5%
e 9909735
 
7.1%
, 7754437
 
5.6%
n 5898204
 
4.2%
t 5700750
 
4.1%
r 5260304
 
3.8%
a 5240569
 
3.8%
i 5164333
 
3.7%
o 4182914
 
3.0%
l 3994458
 
2.9%
Other values (54) 66311876
47.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 139667346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
20249766
 
14.5%
e 9909735
 
7.1%
, 7754437
 
5.6%
n 5898204
 
4.2%
t 5700750
 
4.1%
r 5260304
 
3.8%
a 5240569
 
3.8%
i 5164333
 
3.7%
o 4182914
 
3.0%
l 3994458
 
2.9%
Other values (54) 66311876
47.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 139667346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
20249766
 
14.5%
e 9909735
 
7.1%
, 7754437
 
5.6%
n 5898204
 
4.2%
t 5700750
 
4.1%
r 5260304
 
3.8%
a 5240569
 
3.8%
i 5164333
 
3.7%
o 4182914
 
3.0%
l 3994458
 
2.9%
Other values (54) 66311876
47.5%

birth_month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5316572
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:13.806103image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.5738005
Coefficient of variation (CV)0.54715065
Kurtosis-1.3147905
Mean6.5316572
Median Absolute Deviation (MAD)3
Skewness-0.033964539
Sum22274878
Variance12.77205
MonotonicityNot monotonic
2024-12-03T07:51:13.932079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 352874
10.3%
1 320493
9.4%
2 308628
9.0%
10 297865
8.7%
5 292364
8.6%
3 291414
8.5%
12 285375
8.4%
8 282787
8.3%
9 269726
7.9%
7 260927
7.7%
Other values (2) 447842
13.1%
ValueCountFrequency (%)
1 320493
9.4%
2 308628
9.0%
3 291414
8.5%
4 243534
7.1%
5 292364
8.6%
6 204308
6.0%
7 260927
7.7%
8 282787
8.3%
9 269726
7.9%
10 297865
8.7%
ValueCountFrequency (%)
12 285375
8.4%
11 352874
10.3%
10 297865
8.7%
9 269726
7.9%
8 282787
8.3%
7 260927
7.7%
6 204308
6.0%
5 292364
8.6%
4 243534
7.1%
3 291414
8.5%

birth_year
Real number (ℝ)

High correlation 

Distinct75
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1966.8056
Minimum1918
Maximum1997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:14.105468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1918
5-th percentile1936
Q11957
median1969
Q31979
95-th percentile1989
Maximum1997
Range79
Interquartile range (IQR)22

Descriptive statistics

Standard deviation16.078873
Coefficient of variation (CV)0.0081751206
Kurtosis-0.25295708
Mean1966.8056
Median Absolute Deviation (MAD)11
Skewness-0.55253667
Sum6.7073872 × 109
Variance258.53015
MonotonicityNot monotonic
2024-12-03T07:51:14.280431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1972 112478
 
3.3%
1970 109902
 
3.2%
1968 102995
 
3.0%
1961 92494
 
2.7%
1967 89674
 
2.6%
1977 88191
 
2.6%
1965 86852
 
2.5%
1978 85786
 
2.5%
1973 85588
 
2.5%
1975 84749
 
2.5%
Other values (65) 2471586
72.5%
ValueCountFrequency (%)
1918 3268
 
0.1%
1920 2849
 
0.1%
1921 12556
0.4%
1926 11182
0.3%
1927 8189
0.2%
1928 4738
 
0.1%
1929 16202
0.5%
1930 6252
 
0.2%
1931 18017
0.5%
1932 17805
0.5%
ValueCountFrequency (%)
1997 356
 
< 0.1%
1996 5979
 
0.2%
1995 16768
 
0.5%
1994 19136
 
0.6%
1993 27831
 
0.8%
1992 29940
0.9%
1991 35940
1.1%
1990 28568
0.8%
1989 48321
1.4%
1988 70989
2.1%

credit_card_count
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5825256
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:14.440279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6105396
Coefficient of variation (CV)0.44955427
Kurtosis-0.13352642
Mean3.5825256
Median Absolute Deviation (MAD)1
Skewness0.36203334
Sum12217469
Variance2.593838
MonotonicityNot monotonic
2024-12-03T07:51:14.560284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 822001
24.1%
3 816782
24.0%
2 496463
14.6%
5 493601
14.5%
1 375033
11.0%
6 255651
 
7.5%
7 102760
 
3.0%
8 42107
 
1.2%
9 5897
 
0.2%
ValueCountFrequency (%)
1 375033
11.0%
2 496463
14.6%
3 816782
24.0%
4 822001
24.1%
5 493601
14.5%
6 255651
 
7.5%
7 102760
 
3.0%
8 42107
 
1.2%
9 5897
 
0.2%
ValueCountFrequency (%)
9 5897
 
0.2%
8 42107
 
1.2%
7 102760
 
3.0%
6 255651
 
7.5%
5 493601
14.5%
4 822001
24.1%
3 816782
24.0%
2 496463
14.6%
1 375033
11.0%

credit_score
Real number (ℝ)

Distinct302
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean711.79367
Minimum488
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:14.710798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum488
5-th percentile590
Q1683
median713
Q3755
95-th percentile816
Maximum850
Range362
Interquartile range (IQR)72

Descriptive statistics

Standard deviation66.828906
Coefficient of variation (CV)0.093888031
Kurtosis0.6064792
Mean711.79367
Median Absolute Deviation (MAD)35
Skewness-0.51269585
Sum2.4274264 × 109
Variance4466.1027
MonotonicityNot monotonic
2024-12-03T07:51:14.964742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850 58694
 
1.7%
683 46126
 
1.4%
685 44234
 
1.3%
698 43034
 
1.3%
693 42071
 
1.2%
706 37400
 
1.1%
686 36807
 
1.1%
689 36252
 
1.1%
684 34852
 
1.0%
721 34410
 
1.0%
Other values (292) 2996415
87.9%
ValueCountFrequency (%)
488 1048
 
< 0.1%
489 937
 
< 0.1%
490 2274
 
0.1%
491 3921
0.1%
498 3392
0.1%
500 3798
0.1%
501 319
 
< 0.1%
503 3538
0.1%
505 6326
0.2%
506 1624
 
< 0.1%
ValueCountFrequency (%)
850 58694
1.7%
849 4417
 
0.1%
847 2087
 
0.1%
846 1203
 
< 0.1%
842 2578
 
0.1%
841 4471
 
0.1%
840 5540
 
0.2%
839 1495
 
< 0.1%
838 7078
 
0.2%
836 4418
 
0.1%

current_age
Real number (ℝ)

High correlation 

Distinct76
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.378905
Minimum22
Maximum101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:15.161843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile30
Q140
median50
Q362
95-th percentile83
Maximum101
Range79
Interquartile range (IQR)22

Descriptive statistics

Standard deviation16.085196
Coefficient of variation (CV)0.30709301
Kurtosis-0.26013945
Mean52.378905
Median Absolute Deviation (MAD)11
Skewness0.5528775
Sum1.7862752 × 108
Variance258.73352
MonotonicityNot monotonic
2024-12-03T07:51:15.356207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 118915
 
3.5%
51 99298
 
2.9%
49 94971
 
2.8%
48 89049
 
2.6%
42 87448
 
2.6%
50 87271
 
2.6%
54 86324
 
2.5%
52 85592
 
2.5%
43 83610
 
2.5%
59 83204
 
2.4%
Other values (66) 2494613
73.1%
ValueCountFrequency (%)
22 356
 
< 0.1%
23 4589
 
0.1%
24 14867
 
0.4%
25 17274
 
0.5%
26 23190
 
0.7%
27 28015
0.8%
28 33138
1.0%
29 37110
1.1%
30 49949
1.5%
31 63441
1.9%
ValueCountFrequency (%)
101 3268
 
0.1%
99 2849
 
0.1%
98 12556
0.4%
94 9436
0.3%
93 1746
 
0.1%
92 8189
0.2%
91 8734
0.3%
90 12206
0.4%
89 9473
0.3%
88 17558
0.5%

email
Text

Distinct1530
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:15.635659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length33
Median length31
Mean length24.655188
Min length19

Characters and Unicode

Total characters84081464
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowjaziel.howard@example.com
2nd rowjaziel.howard@example.com
3rd rowjaziel.howard@example.com
4th rowjaziel.howard@example.com
5th rowjaziel.howard@example.com
ValueCountFrequency (%)
keegan.perry@example.com 9915
 
0.3%
simeon.cruz@example.com 8768
 
0.3%
jaxon.cook@example.com 8754
 
0.3%
zachariah.fernandez@example.com 8602
 
0.3%
alora.white@example.com 8478
 
0.2%
rory.nelson@example.com 8224
 
0.2%
oscar.sanders@example.com 8195
 
0.2%
vada.fernandez@example.com 8044
 
0.2%
alina.harris@example.com 7845
 
0.2%
gordon.collins@example.com 7473
 
0.2%
Other values (1520) 3325997
97.5%
2024-12-03T07:51:16.029754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 11161064
13.3%
a 8435570
 
10.0%
m 8287569
 
9.9%
. 6820590
 
8.1%
l 6629433
 
7.9%
o 5754409
 
6.8%
c 4384130
 
5.2%
p 3966345
 
4.7%
x 3590156
 
4.3%
r 3496516
 
4.2%
Other values (19) 21555682
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84081464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 11161064
13.3%
a 8435570
 
10.0%
m 8287569
 
9.9%
. 6820590
 
8.1%
l 6629433
 
7.9%
o 5754409
 
6.8%
c 4384130
 
5.2%
p 3966345
 
4.7%
x 3590156
 
4.3%
r 3496516
 
4.2%
Other values (19) 21555682
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84081464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 11161064
13.3%
a 8435570
 
10.0%
m 8287569
 
9.9%
. 6820590
 
8.1%
l 6629433
 
7.9%
o 5754409
 
6.8%
c 4384130
 
5.2%
p 3966345
 
4.7%
x 3590156
 
4.3%
r 3496516
 
4.2%
Other values (19) 21555682
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84081464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 11161064
13.3%
a 8435570
 
10.0%
m 8287569
 
9.9%
. 6820590
 
8.1%
l 6629433
 
7.9%
o 5754409
 
6.8%
c 4384130
 
5.2%
p 3966345
 
4.7%
x 3590156
 
4.3%
r 3496516
 
4.2%
Other values (19) 21555682
25.6%
Distinct1066
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:16.318499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.8523796
Min length2

Characters and Unicode

Total characters19958341
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowJaziel
2nd rowJaziel
3rd rowJaziel
4th rowJaziel
5th rowJaziel
ValueCountFrequency (%)
simeon 14249
 
0.4%
dallas 12909
 
0.4%
maggie 12039
 
0.4%
cristiano 11733
 
0.3%
opal 11725
 
0.3%
maya 11573
 
0.3%
keegan 11517
 
0.3%
jaxon 11170
 
0.3%
colt 10474
 
0.3%
ryan 10321
 
0.3%
Other values (1056) 3292585
96.5%
2024-12-03T07:51:17.057765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2795601
14.0%
e 2018158
 
10.1%
n 1739860
 
8.7%
i 1639408
 
8.2%
l 1461405
 
7.3%
r 1151826
 
5.8%
o 898433
 
4.5%
y 806821
 
4.0%
s 682229
 
3.4%
t 510379
 
2.6%
Other values (42) 6254221
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19958341
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2795601
14.0%
e 2018158
 
10.1%
n 1739860
 
8.7%
i 1639408
 
8.2%
l 1461405
 
7.3%
r 1151826
 
5.8%
o 898433
 
4.5%
y 806821
 
4.0%
s 682229
 
3.4%
t 510379
 
2.6%
Other values (42) 6254221
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19958341
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2795601
14.0%
e 2018158
 
10.1%
n 1739860
 
8.7%
i 1639408
 
8.2%
l 1461405
 
7.3%
r 1151826
 
5.8%
o 898433
 
4.5%
y 806821
 
4.0%
s 682229
 
3.4%
t 510379
 
2.6%
Other values (42) 6254221
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19958341
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2795601
14.0%
e 2018158
 
10.1%
n 1739860
 
8.7%
i 1639408
 
8.2%
l 1461405
 
7.3%
r 1151826
 
5.8%
o 898433
 
4.5%
y 806821
 
4.0%
s 682229
 
3.4%
t 510379
 
2.6%
Other values (42) 6254221
31.3%

gender
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.0 MiB
Female
1764157 
Male
1646138 

Length

Max length6
Median length6
Mean length5.0346067
Min length4

Characters and Unicode

Total characters17169494
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 1764157
51.7%
Male 1646138
48.3%

Length

2024-12-03T07:51:17.248025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T07:51:17.403905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
female 1764157
51.7%
male 1646138
48.3%

Most occurring characters

ValueCountFrequency (%)
e 5174452
30.1%
a 3410295
19.9%
l 3410295
19.9%
F 1764157
 
10.3%
m 1764157
 
10.3%
M 1646138
 
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17169494
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5174452
30.1%
a 3410295
19.9%
l 3410295
19.9%
F 1764157
 
10.3%
m 1764157
 
10.3%
M 1646138
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17169494
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5174452
30.1%
a 3410295
19.9%
l 3410295
19.9%
F 1764157
 
10.3%
m 1764157
 
10.3%
M 1646138
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17169494
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5174452
30.1%
a 3410295
19.9%
l 3410295
19.9%
F 1764157
 
10.3%
m 1764157
 
10.3%
M 1646138
 
9.6%
Distinct126
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:17.710086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.8028083
Min length2

Characters and Unicode

Total characters19789288
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHoward
2nd rowHoward
3rd rowHoward
4th rowHoward
5th rowHoward
ValueCountFrequency (%)
phillips 55713
 
1.6%
torres 54999
 
1.6%
faraday 43979
 
1.3%
abe 43299
 
1.3%
cook 43125
 
1.3%
nguyen 42914
 
1.3%
howard 42620
 
1.2%
king 42323
 
1.2%
martin 41257
 
1.2%
ward 40669
 
1.2%
Other values (116) 2959397
86.8%
2024-12-03T07:51:18.233249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2075024
 
10.5%
r 1852894
 
9.4%
a 1602741
 
8.1%
l 1377880
 
7.0%
o 1355519
 
6.8%
n 1220091
 
6.2%
s 1160576
 
5.9%
i 1119563
 
5.7%
d 516805
 
2.6%
h 485127
 
2.5%
Other values (37) 7023068
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19789288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2075024
 
10.5%
r 1852894
 
9.4%
a 1602741
 
8.1%
l 1377880
 
7.0%
o 1355519
 
6.8%
n 1220091
 
6.2%
s 1160576
 
5.9%
i 1119563
 
5.7%
d 516805
 
2.6%
h 485127
 
2.5%
Other values (37) 7023068
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19789288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2075024
 
10.5%
r 1852894
 
9.4%
a 1602741
 
8.1%
l 1377880
 
7.0%
o 1355519
 
6.8%
n 1220091
 
6.2%
s 1160576
 
5.9%
i 1119563
 
5.7%
d 516805
 
2.6%
h 485127
 
2.5%
Other values (37) 7023068
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19789288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2075024
 
10.5%
r 1852894
 
9.4%
a 1602741
 
8.1%
l 1377880
 
7.0%
o 1355519
 
6.8%
n 1220091
 
6.2%
s 1160576
 
5.9%
i 1119563
 
5.7%
d 516805
 
2.6%
h 485127
 
2.5%
Other values (37) 7023068
35.5%

latitude
Real number (ℝ)

High correlation 

Distinct855
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.366956
Minimum21.3
Maximum61.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:18.411336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum21.3
5-th percentile28.3
Q133.88
median38.35
Q341.07
95-th percentile44.65
Maximum61.2
Range39.9
Interquartile range (IQR)7.19

Descriptive statistics

Standard deviation5.0917774
Coefficient of variation (CV)0.13626417
Kurtosis-0.19981123
Mean37.366956
Median Absolute Deviation (MAD)3.55
Skewness-0.36982755
Sum1.2743234 × 108
Variance25.926197
MonotonicityNot monotonic
2024-12-03T07:51:18.643225image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.76 43988
 
1.3%
25.77 30204
 
0.9%
40.64 26966
 
0.8%
40.84 26308
 
0.8%
29.45 23473
 
0.7%
41.47 20509
 
0.6%
41.83 20067
 
0.6%
40.71 19221
 
0.6%
35.11 16904
 
0.5%
39.77 16596
 
0.5%
Other values (845) 3166059
92.8%
ValueCountFrequency (%)
21.3 1494
 
< 0.1%
21.31 3954
 
0.1%
21.34 2014
 
0.1%
21.39 1156
 
< 0.1%
21.4 1269
 
< 0.1%
21.41 2377
 
0.1%
21.44 2721
 
0.1%
22.21 1340
 
< 0.1%
25.77 30204
0.9%
25.92 3469
 
0.1%
ValueCountFrequency (%)
61.2 917
 
< 0.1%
48.53 1119
 
< 0.1%
48.28 4579
0.1%
48.19 1891
 
0.1%
47.94 1464
 
< 0.1%
47.91 4123
0.1%
47.83 1072
 
< 0.1%
47.79 9001
0.3%
47.75 3654
0.1%
47.69 632
 
< 0.1%

longitude
Real number (ℝ)

High correlation 

Distinct1021
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-91.386768
Minimum-159.41
Maximum-68.67
Zeros0
Zeros (%)0.0%
Negative3410295
Negative (%)100.0%
Memory size26.0 MiB
2024-12-03T07:51:18.919099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-159.41
5-th percentile-122.06
Q1-97.34
median-86.28
Q3-80.08
95-th percentile-73.4
Maximum-68.67
Range90.74
Interquartile range (IQR)17.26

Descriptive statistics

Standard deviation16.207777
Coefficient of variation (CV)-0.17735365
Kurtosis0.44073609
Mean-91.386768
Median Absolute Deviation (MAD)9.1
Skewness-1.015478
Sum-3.1165584 × 108
Variance262.69204
MonotonicityNot monotonic
2024-12-03T07:51:19.166393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-95.38 47951
 
1.4%
-73.94 26966
 
0.8%
-80.2 26389
 
0.8%
-87.68 20067
 
0.6%
-90 19692
 
0.6%
-80.13 19643
 
0.6%
-98.5 18468
 
0.5%
-86.14 16596
 
0.5%
-73.87 16465
 
0.5%
-106.62 15934
 
0.5%
Other values (1011) 3182124
93.3%
ValueCountFrequency (%)
-159.41 1340
 
< 0.1%
-158.18 2721
0.1%
-158.01 5110
0.1%
-157.85 1494
 
< 0.1%
-157.79 2377
0.1%
-157.73 1269
 
< 0.1%
-157.72 2014
 
0.1%
-149.82 917
 
< 0.1%
-124.16 1600
 
< 0.1%
-124.14 2009
 
0.1%
ValueCountFrequency (%)
-68.67 2012
0.1%
-69.83 2217
0.1%
-70.08 1517
< 0.1%
-70.24 2638
0.1%
-70.3 2820
0.1%
-70.33 2672
0.1%
-70.7 1235
 
< 0.1%
-70.73 2485
0.1%
-70.86 3149
0.1%
-70.87 3513
0.1%

per_capita_income
Real number (ℝ)

High correlation 

Distinct1386
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23885.169
Minimum0
Maximum163145
Zeros17017
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:19.338682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13047
Q117013
median21156
Q327544
95-th percentile43827
Maximum163145
Range163145
Interquartile range (IQR)10531

Descriptive statistics

Standard deviation11602.533
Coefficient of variation (CV)0.48576305
Kurtosis24.596456
Mean23885.169
Median Absolute Deviation (MAD)4735
Skewness3.511435
Sum8.1455473 × 1010
Variance1.3461876 × 108
MonotonicityNot monotonic
2024-12-03T07:51:19.527383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17017
 
0.5%
16901 12336
 
0.4%
26137 11669
 
0.3%
24044 10247
 
0.3%
25537 9579
 
0.3%
19851 8900
 
0.3%
25258 8881
 
0.3%
21156 8768
 
0.3%
15385 8764
 
0.3%
16892 8478
 
0.2%
Other values (1376) 3305656
96.9%
ValueCountFrequency (%)
0 17017
0.5%
6201 810
 
< 0.1%
8155 1450
 
< 0.1%
8491 1656
 
< 0.1%
8658 756
 
< 0.1%
9284 2462
 
0.1%
9389 1770
 
0.1%
9710 789
 
< 0.1%
9995 892
 
< 0.1%
10016 1828
 
0.1%
ValueCountFrequency (%)
163145 1746
 
0.1%
137428 2732
 
0.1%
96516 1423
 
< 0.1%
95039 8115
0.2%
94302 2361
 
0.1%
91180 4207
0.1%
79100 1563
 
< 0.1%
76725 4019
0.1%
75378 2544
 
0.1%
74205 4475
0.1%

retirement_age
Real number (ℝ)

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.394789
Minimum50
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:19.718763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile60
Q165
median66
Q368
95-th percentile72
Maximum79
Range29
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.5649488
Coefficient of variation (CV)0.053693201
Kurtosis1.5586734
Mean66.394789
Median Absolute Deviation (MAD)2
Skewness-0.38431589
Sum2.2642582 × 108
Variance12.70886
MonotonicityNot monotonic
2024-12-03T07:51:19.877535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
65 555105
16.3%
66 540284
15.8%
67 480962
14.1%
68 343377
10.1%
69 262277
7.7%
64 182498
 
5.4%
70 163014
 
4.8%
71 140729
 
4.1%
63 104728
 
3.1%
62 104094
 
3.1%
Other values (18) 533227
15.6%
ValueCountFrequency (%)
50 3226
 
0.1%
53 3843
 
0.1%
54 11215
 
0.3%
55 8685
 
0.3%
56 7921
 
0.2%
57 32434
1.0%
58 32678
1.0%
59 52052
1.5%
60 58530
1.7%
61 79231
2.3%
ValueCountFrequency (%)
79 2851
 
0.1%
78 8093
 
0.2%
77 1718
 
0.1%
76 1802
 
0.1%
75 29027
 
0.9%
74 31673
 
0.9%
73 67478
2.0%
72 100770
3.0%
71 140729
4.1%
70 163014
4.8%

total_debt
Real number (ℝ)

High correlation  Zeros 

Distinct1446
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59689.786
Minimum0
Maximum461854
Zeros175422
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:20.063459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119299
median53023
Q386171
95-th percentile151030
Maximum461854
Range461854
Interquartile range (IQR)66872

Descriptive statistics

Standard deviation52635.215
Coefficient of variation (CV)0.88181276
Kurtosis6.6309351
Mean59689.786
Median Absolute Deviation (MAD)33619
Skewness1.7234573
Sum2.0355978 × 1011
Variance2.7704659 × 109
MonotonicityNot monotonic
2024-12-03T07:51:20.244831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 175422
 
5.1%
54634 11582
 
0.3%
44263 8768
 
0.3%
66148 8754
 
0.3%
370 8602
 
0.3%
907 8478
 
0.2%
6332 8195
 
0.2%
15899 8044
 
0.2%
4344 7845
 
0.2%
88165 7473
 
0.2%
Other values (1436) 3157132
92.6%
ValueCountFrequency (%)
0 175422
5.1%
5 753
 
< 0.1%
49 4042
 
0.1%
66 4046
 
0.1%
69 4215
 
0.1%
92 2496
 
0.1%
93 1558
 
< 0.1%
182 1414
 
< 0.1%
196 5588
 
0.2%
216 2567
 
0.1%
ValueCountFrequency (%)
461854 4207
0.1%
437533 1423
 
< 0.1%
328089 3079
0.1%
317964 1563
 
< 0.1%
307856 3077
0.1%
265319 1597
 
< 0.1%
255288 2581
0.1%
252106 1527
 
< 0.1%
247623 2692
0.1%
242379 6326
0.2%

yearly_income
Real number (ℝ)

High correlation 

Distinct1502
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46645.735
Minimum1
Maximum280199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:20.424519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22583
Q133076
median41109
Q354122
95-th percentile87030
Maximum280199
Range280198
Interquartile range (IQR)21046

Descriptive statistics

Standard deviation23705.569
Coefficient of variation (CV)0.50820442
Kurtosis17.337534
Mean46645.735
Median Absolute Deviation (MAD)9964
Skewness2.9797885
Sum1.5907572 × 1011
Variance5.6195399 × 108
MonotonicityNot monotonic
2024-12-03T07:51:20.591062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34456 9915
 
0.3%
43133 8768
 
0.3%
49023 8754
 
0.3%
33869 8602
 
0.3%
34441 8478
 
0.2%
73067 8195
 
0.2%
41513 8044
 
0.2%
99825 7845
 
0.2%
60746 7473
 
0.2%
53702 7450
 
0.2%
Other values (1492) 3326771
97.6%
ValueCountFrequency (%)
1 1142
 
< 0.1%
2 2124
0.1%
3 753
 
< 0.1%
4 2067
0.1%
399 483
 
< 0.1%
553 444
 
< 0.1%
645 786
 
< 0.1%
920 1412
 
< 0.1%
1426 3932
0.1%
1785 976
 
< 0.1%
ValueCountFrequency (%)
280199 2732
0.1%
249925 1746
 
0.1%
196784 1423
 
< 0.1%
193773 5224
0.2%
193768 2891
0.1%
192269 2361
0.1%
185909 4207
0.1%
162709 4475
0.1%
161276 1563
 
< 0.1%
153691 2544
0.1%

card_id
Real number (ℝ)

High correlation 

Distinct4089
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3077.7428
Minimum0
Maximum6143
Zeros443
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:20.768542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile281
Q11565
median3090
Q34573
95-th percentile5825
Maximum6143
Range6143
Interquartile range (IQR)3008

Descriptive statistics

Standard deviation1768.9366
Coefficient of variation (CV)0.57475128
Kurtosis-1.1788577
Mean3077.7428
Median Absolute Deviation (MAD)1504
Skewness-0.0074526992
Sum1.0496011 × 1010
Variance3129136.7
MonotonicityNot monotonic
2024-12-03T07:51:20.941803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3310 7450
 
0.2%
2959 7052
 
0.2%
5805 6326
 
0.2%
349 6148
 
0.2%
5160 5557
 
0.2%
108 5533
 
0.2%
2435 5510
 
0.2%
165 5224
 
0.2%
1192 5210
 
0.2%
998 5097
 
0.1%
Other values (4079) 3351188
98.3%
ValueCountFrequency (%)
0 443
 
< 0.1%
1 381
 
< 0.1%
2 365
 
< 0.1%
3 1112
< 0.1%
6 413
 
< 0.1%
7 386
 
< 0.1%
8 241
 
< 0.1%
9 82
 
< 0.1%
10 1390
< 0.1%
11 1458
< 0.1%
ValueCountFrequency (%)
6143 1457
< 0.1%
6142 983
< 0.1%
6141 1003
< 0.1%
6140 429
 
< 0.1%
6139 482
 
< 0.1%
6138 853
< 0.1%
6137 515
 
< 0.1%
6136 449
 
< 0.1%
6135 736
< 0.1%
6134 1568
< 0.1%
Distinct267
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.0 MiB
Minimum1991-01-01 00:00:00
Maximum2017-10-01 00:00:00
2024-12-03T07:51:21.127562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-03T07:51:21.285589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

card_brand
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.0 MiB
Mastercard
1855091 
Visa
1259298 
Amex
207022 
Discover
 
88884

Length

Max length10
Median length10
Mean length7.3680611
Min length4

Characters and Unicode

Total characters25127262
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMastercard
2nd rowMastercard
3rd rowMastercard
4th rowMastercard
5th rowMastercard

Common Values

ValueCountFrequency (%)
Mastercard 1855091
54.4%
Visa 1259298
36.9%
Amex 207022
 
6.1%
Discover 88884
 
2.6%

Length

2024-12-03T07:51:21.443120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T07:51:21.575088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
mastercard 1855091
54.4%
visa 1259298
36.9%
amex 207022
 
6.1%
discover 88884
 
2.6%

Most occurring characters

ValueCountFrequency (%)
a 4969480
19.8%
r 3799066
15.1%
s 3203273
12.7%
e 2150997
8.6%
c 1943975
 
7.7%
M 1855091
 
7.4%
t 1855091
 
7.4%
d 1855091
 
7.4%
i 1348182
 
5.4%
V 1259298
 
5.0%
Other values (6) 887718
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25127262
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4969480
19.8%
r 3799066
15.1%
s 3203273
12.7%
e 2150997
8.6%
c 1943975
 
7.7%
M 1855091
 
7.4%
t 1855091
 
7.4%
d 1855091
 
7.4%
i 1348182
 
5.4%
V 1259298
 
5.0%
Other values (6) 887718
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25127262
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4969480
19.8%
r 3799066
15.1%
s 3203273
12.7%
e 2150997
8.6%
c 1943975
 
7.7%
M 1855091
 
7.4%
t 1855091
 
7.4%
d 1855091
 
7.4%
i 1348182
 
5.4%
V 1259298
 
5.0%
Other values (6) 887718
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25127262
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4969480
19.8%
r 3799066
15.1%
s 3203273
12.7%
e 2150997
8.6%
c 1943975
 
7.7%
M 1855091
 
7.4%
t 1855091
 
7.4%
d 1855091
 
7.4%
i 1348182
 
5.4%
V 1259298
 
5.0%
Other values (6) 887718
 
3.5%
Distinct121
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.0 MiB
Minimum2014-12-01 00:00:00
Maximum2024-12-01 00:00:00
2024-12-03T07:51:21.715991image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-03T07:51:21.875210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

card_index
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2959486
Minimum0
Maximum8
Zeros1264062
Zeros (%)37.1%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:22.010818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.373014
Coefficient of variation (CV)1.0594664
Kurtosis0.86915861
Mean1.2959486
Median Absolute Deviation (MAD)1
Skewness1.08584
Sum4419567
Variance1.8851675
MonotonicityNot monotonic
2024-12-03T07:51:22.138068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1264062
37.1%
1 897832
26.3%
2 615658
18.1%
3 374578
 
11.0%
4 160624
 
4.7%
5 68097
 
2.0%
6 22868
 
0.7%
7 6112
 
0.2%
8 464
 
< 0.1%
ValueCountFrequency (%)
0 1264062
37.1%
1 897832
26.3%
2 615658
18.1%
3 374578
 
11.0%
4 160624
 
4.7%
5 68097
 
2.0%
6 22868
 
0.7%
7 6112
 
0.2%
8 464
 
< 0.1%
ValueCountFrequency (%)
8 464
 
< 0.1%
7 6112
 
0.2%
6 22868
 
0.7%
5 68097
 
2.0%
4 160624
 
4.7%
3 374578
 
11.0%
2 615658
18.1%
1 897832
26.3%
0 1264062
37.1%

card_number
Real number (ℝ)

High correlation 

Distinct4089
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8410033 × 1015
Minimum3.0060978 × 1014
Maximum6.9944982 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:22.305558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.0060978 × 1014
5-th percentile3.7703538 × 1014
Q14.5085837 × 1015
median5.1182281 × 1015
Q35.5796045 × 1015
95-th percentile5.9494413 × 1015
Maximum6.9944982 × 1015
Range6.6938884 × 1015
Interquartile range (IQR)1.0710208 × 1015

Descriptive statistics

Standard deviation1.2860991 × 1015
Coefficient of variation (CV)0.26566788
Kurtosis6.2175986
Mean4.8410033 × 1015
Median Absolute Deviation (MAD)5.2056488 × 1014
Skewness-2.4336304
Sum-5.8660278 × 1017
Variance1.6540509 × 1030
MonotonicityNot monotonic
2024-12-03T07:51:22.476089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.412731782 × 10157450
 
0.2%
4.049783428 × 10157052
 
0.2%
5.809446324 × 10156326
 
0.2%
5.309940543 × 10156148
 
0.2%
3.523973604 × 10145557
 
0.2%
5.595562389 × 10155533
 
0.2%
5.206850869 × 10155510
 
0.2%
5.476407228 × 10155224
 
0.2%
3.577316041 × 10145210
 
0.2%
5.552351758 × 10155097
 
0.1%
Other values (4079) 3351188
98.3%
ValueCountFrequency (%)
3.006097828 × 10141003
 
< 0.1%
3.020142536 × 10142159
0.1%
3.021286918 × 1014818
 
< 0.1%
3.023241546 × 10141541
< 0.1%
3.027375882 × 1014255
 
< 0.1%
3.030847201 × 1014541
 
< 0.1%
3.033882871 × 10142769
0.1%
3.036398784 × 1014183
 
< 0.1%
3.040323297 × 1014258
 
< 0.1%
3.040496397 × 1014542
 
< 0.1%
ValueCountFrequency (%)
6.994498161 × 10151528
< 0.1%
6.994217665 × 1015614
< 0.1%
6.987061746 × 1015507
 
< 0.1%
6.983129593 × 10151491
< 0.1%
6.977627305 × 1015262
 
< 0.1%
6.974497426 × 1015423
 
< 0.1%
6.973723422 × 1015710
< 0.1%
6.969946254 × 1015717
< 0.1%
6.967609498 × 1015778
< 0.1%
6.955304802 × 1015237
 
< 0.1%

card_on_dark_web
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
False
3410295 
ValueCountFrequency (%)
False 3410295
100.0%
2024-12-03T07:51:22.612825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

card_type
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.0 MiB
Debit
2134230 
Credit
1041259 
Debit (Prepaid)
234806 

Length

Max length15
Median length5
Mean length5.9938492
Min length5

Characters and Unicode

Total characters20440794
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCredit
2nd rowCredit
3rd rowCredit
4th rowCredit
5th rowCredit

Common Values

ValueCountFrequency (%)
Debit 2134230
62.6%
Credit 1041259
30.5%
Debit (Prepaid) 234806
 
6.9%

Length

2024-12-03T07:51:22.728887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T07:51:22.849481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
debit 2369036
65.0%
credit 1041259
28.6%
prepaid 234806
 
6.4%

Most occurring characters

ValueCountFrequency (%)
e 3645101
17.8%
i 3645101
17.8%
t 3410295
16.7%
D 2369036
11.6%
b 2369036
11.6%
r 1276065
 
6.2%
d 1276065
 
6.2%
C 1041259
 
5.1%
234806
 
1.1%
( 234806
 
1.1%
Other values (4) 939224
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20440794
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3645101
17.8%
i 3645101
17.8%
t 3410295
16.7%
D 2369036
11.6%
b 2369036
11.6%
r 1276065
 
6.2%
d 1276065
 
6.2%
C 1041259
 
5.1%
234806
 
1.1%
( 234806
 
1.1%
Other values (4) 939224
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20440794
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3645101
17.8%
i 3645101
17.8%
t 3410295
16.7%
D 2369036
11.6%
b 2369036
11.6%
r 1276065
 
6.2%
d 1276065
 
6.2%
C 1041259
 
5.1%
234806
 
1.1%
( 234806
 
1.1%
Other values (4) 939224
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20440794
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3645101
17.8%
i 3645101
17.8%
t 3410295
16.7%
D 2369036
11.6%
b 2369036
11.6%
r 1276065
 
6.2%
d 1276065
 
6.2%
C 1041259
 
5.1%
234806
 
1.1%
( 234806
 
1.1%
Other values (4) 939224
 
4.6%

credit_limit
Real number (ℝ)

High correlation 

Distinct2620
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15414.282
Minimum0
Maximum141391
Zeros15599
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:22.982503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile77
Q17700
median13400
Q320765
95-th percentile36425
Maximum141391
Range141391
Interquartile range (IQR)13065

Descriptive statistics

Standard deviation12114.444
Coefficient of variation (CV)0.78592336
Kurtosis13.297339
Mean15414.282
Median Absolute Deviation (MAD)6346
Skewness2.2783409
Sum5.2567248 × 1010
Variance1.4675976 × 108
MonotonicityNot monotonic
2024-12-03T07:51:23.154952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10600 15815
 
0.5%
0 15599
 
0.5%
8700 13665
 
0.4%
9800 13657
 
0.4%
8800 13113
 
0.4%
8900 12005
 
0.4%
9700 11809
 
0.3%
9500 11731
 
0.3%
12700 11695
 
0.3%
65 11151
 
0.3%
Other values (2610) 3280055
96.2%
ValueCountFrequency (%)
0 15599
0.5%
1 156
 
< 0.1%
3 567
 
< 0.1%
4 436
 
< 0.1%
8 95
 
< 0.1%
9 484
 
< 0.1%
10 80
 
< 0.1%
11 1051
 
< 0.1%
12 367
 
< 0.1%
13 304
 
< 0.1%
ValueCountFrequency (%)
141391 630
< 0.1%
137669 1528
< 0.1%
132439 622
< 0.1%
130971 363
 
< 0.1%
125723 634
< 0.1%
98100 212
 
< 0.1%
97352 587
 
< 0.1%
96637 596
 
< 0.1%
92828 39
 
< 0.1%
88743 1470
< 0.1%

cvv_code
Real number (ℝ)

High correlation 

Distinct986
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean491.52517
Minimum0
Maximum999
Zeros2731
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:24.201506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile45
Q1237
median492
Q3740
95-th percentile950
Maximum999
Range999
Interquartile range (IQR)503

Descriptive statistics

Standard deviation290.65588
Coefficient of variation (CV)0.59133469
Kurtosis-1.2169936
Mean491.52517
Median Absolute Deviation (MAD)250
Skewness0.028897842
Sum1.6762458 × 109
Variance84480.841
MonotonicityNot monotonic
2024-12-03T07:51:24.352312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 14124
 
0.4%
269 14122
 
0.4%
265 12603
 
0.4%
278 10437
 
0.3%
681 10357
 
0.3%
152 10226
 
0.3%
594 10120
 
0.3%
231 10014
 
0.3%
599 9884
 
0.3%
25 9612
 
0.3%
Other values (976) 3298796
96.7%
ValueCountFrequency (%)
0 2731
0.1%
1 1370
 
< 0.1%
2 1874
0.1%
3 1880
0.1%
4 662
 
< 0.1%
5 856
 
< 0.1%
6 783
 
< 0.1%
7 4531
0.1%
8 2420
0.1%
9 2747
0.1%
ValueCountFrequency (%)
999 2203
 
0.1%
998 795
 
< 0.1%
997 512
 
< 0.1%
996 3266
0.1%
995 4024
0.1%
994 2657
0.1%
993 3452
0.1%
992 6380
0.2%
991 4578
0.1%
990 3902
0.1%

has_chip
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
True
3069268 
False
341027 
ValueCountFrequency (%)
True 3069268
90.0%
False 341027
 
10.0%
2024-12-03T07:51:24.483663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

number_cards_issued
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.0 MiB
2
1706723 
1
1676846 
3
 
26726

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3410295
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 1706723
50.0%
1 1676846
49.2%
3 26726
 
0.8%

Length

2024-12-03T07:51:24.631044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T07:51:24.885364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 1706723
50.0%
1 1676846
49.2%
3 26726
 
0.8%

Most occurring characters

ValueCountFrequency (%)
2 1706723
50.0%
1 1676846
49.2%
3 26726
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3410295
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1706723
50.0%
1 1676846
49.2%
3 26726
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3410295
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1706723
50.0%
1 1676846
49.2%
3 26726
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3410295
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1706723
50.0%
1 1676846
49.2%
3 26726
 
0.8%

pin_last_changed_year
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.4679
Minimum2002
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:24.999736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2007
Q12010
median2011
Q32013
95-th percentile2016
Maximum2020
Range18
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8111607
Coefficient of variation (CV)0.0013975668
Kurtosis0.25003815
Mean2011.4679
Median Absolute Deviation (MAD)2
Skewness0.23724737
Sum6.8596988 × 109
Variance7.9026246
MonotonicityNot monotonic
2024-12-03T07:51:25.206111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2011 593016
17.4%
2010 525029
15.4%
2012 364802
10.7%
2013 342503
10.0%
2009 333281
9.8%
2014 312642
9.2%
2015 248230
7.3%
2008 210293
 
6.2%
2007 128819
 
3.8%
2016 119917
 
3.5%
Other values (9) 231763
 
6.8%
ValueCountFrequency (%)
2002 545
 
< 0.1%
2003 11442
 
0.3%
2004 7483
 
0.2%
2005 22961
 
0.7%
2006 59489
 
1.7%
2007 128819
 
3.8%
2008 210293
 
6.2%
2009 333281
9.8%
2010 525029
15.4%
2011 593016
17.4%
ValueCountFrequency (%)
2020 22884
 
0.7%
2019 22057
 
0.6%
2018 45231
 
1.3%
2017 39671
 
1.2%
2016 119917
 
3.5%
2015 248230
7.3%
2014 312642
9.2%
2013 342503
10.0%
2012 364802
10.7%
2011 593016
17.4%

transaction_id
Real number (ℝ)

High correlation  Unique 

Distinct3410295
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36632243
Minimum24390796
Maximum48766716
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:25.461059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum24390796
5-th percentile25640490
Q130576474
median36653992
Q342739196
95-th percentile47577959
Maximum48766716
Range24375920
Interquartile range (IQR)12162722

Descriptive statistics

Standard deviation7046989.4
Coefficient of variation (CV)0.19237122
Kurtosis-1.2039583
Mean36632243
Median Absolute Deviation (MAD)6078480
Skewness-2.3031187 × 10-5
Sum1.2492675 × 1014
Variance4.966006 × 1013
MonotonicityNot monotonic
2024-12-03T07:51:25.708579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48682211 1
 
< 0.1%
40600275 1
 
< 0.1%
40593418 1
 
< 0.1%
40593419 1
 
< 0.1%
40593420 1
 
< 0.1%
40593421 1
 
< 0.1%
40593422 1
 
< 0.1%
40593423 1
 
< 0.1%
40593424 1
 
< 0.1%
40593425 1
 
< 0.1%
Other values (3410285) 3410285
> 99.9%
ValueCountFrequency (%)
24390796 1
< 0.1%
24390797 1
< 0.1%
24390798 1
< 0.1%
24390799 1
< 0.1%
24390800 1
< 0.1%
24390801 1
< 0.1%
24390802 1
< 0.1%
24390803 1
< 0.1%
24390804 1
< 0.1%
24390805 1
< 0.1%
ValueCountFrequency (%)
48766716 1
< 0.1%
48766715 1
< 0.1%
48766714 1
< 0.1%
48766713 1
< 0.1%
48766712 1
< 0.1%
48766711 1
< 0.1%
48766710 1
< 0.1%
48766709 1
< 0.1%
48766708 1
< 0.1%
48766707 1
< 0.1%

fraud_detected
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
False
3403435 
True
 
6860
ValueCountFrequency (%)
False 3403435
99.8%
True 6860
 
0.2%
2024-12-03T07:51:25.900352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct9782
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:26.191004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length8.5347725
Min length3

Characters and Unicode

Total characters29106092
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique920 ?
Unique (%)< 0.1%

Sample

1st rowCoushatta
2nd rowBlanchard
3rd rowBienville
4th rowCotton Valley
5th rowCoushatta
ValueCountFrequency (%)
online 428339
 
10.1%
san 69227
 
1.6%
city 67716
 
1.6%
houston 37344
 
0.9%
beach 36967
 
0.9%
park 33998
 
0.8%
new 32634
 
0.8%
saint 30118
 
0.7%
fort 30083
 
0.7%
los 28451
 
0.7%
Other values (8218) 3431397
81.2%
2024-12-03T07:51:26.731470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2400407
 
8.2%
e 2353139
 
8.1%
o 2147888
 
7.4%
n 2033051
 
7.0%
l 1876340
 
6.4%
r 1636742
 
5.6%
i 1625957
 
5.6%
t 1348201
 
4.6%
s 1116827
 
3.8%
N 991941
 
3.4%
Other values (44) 11575599
39.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29106092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2400407
 
8.2%
e 2353139
 
8.1%
o 2147888
 
7.4%
n 2033051
 
7.0%
l 1876340
 
6.4%
r 1636742
 
5.6%
i 1625957
 
5.6%
t 1348201
 
4.6%
s 1116827
 
3.8%
N 991941
 
3.4%
Other values (44) 11575599
39.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29106092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2400407
 
8.2%
e 2353139
 
8.1%
o 2147888
 
7.4%
n 2033051
 
7.0%
l 1876340
 
6.4%
r 1636742
 
5.6%
i 1625957
 
5.6%
t 1348201
 
4.6%
s 1116827
 
3.8%
N 991941
 
3.4%
Other values (44) 11575599
39.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29106092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2400407
 
8.2%
e 2353139
 
8.1%
o 2147888
 
7.4%
n 2033051
 
7.0%
l 1876340
 
6.4%
r 1636742
 
5.6%
i 1625957
 
5.6%
t 1348201
 
4.6%
s 1116827
 
3.8%
N 991941
 
3.4%
Other values (44) 11575599
39.8%

merchant_id
Real number (ℝ)

High correlation 

Distinct45759
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.7914061 × 1017
Minimum-9.2228994 × 1018
Maximum9.2228771 × 1018
Zeros0
Zeros (%)0.0%
Negative1822859
Negative (%)53.5%
Memory size26.0 MiB
2024-12-03T07:51:26.930136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-9.2228994 × 1018
5-th percentile-7.1522583 × 1018
Q1-4.5306007 × 1018
median-7.948098 × 1017
Q33.2157863 × 1018
95-th percentile7.4534879 × 1018
Maximum9.2228771 × 1018
Range-9.675152 × 1014
Interquartile range (IQR)7.746387 × 1018

Descriptive statistics

Standard deviation4.7678509 × 1018
Coefficient of variation (CV)-9.9508387
Kurtosis-1.0670783
Mean-4.7914061 × 1017
Median Absolute Deviation (MAD)3.8991701 × 1018
Skewness0.20461948
Sum1.7742764 × 1018
Variance2.2732402 × 1037
MonotonicityNot monotonic
2024-12-03T07:51:27.138908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.79918998 × 1018152248
 
4.5%
-4.282466774 × 1018149676
 
4.4%
2.02755365 × 1018139659
 
4.1%
-2.088492412 × 1018118291
 
3.5%
-1.288082279 × 101890584
 
2.7%
-5.162038176 × 101889256
 
2.6%
-6.57101047 × 101881067
 
2.4%
1.913477461 × 101878030
 
2.3%
9.703279769 × 101675951
 
2.2%
4.722913069 × 101868144
 
2.0%
Other values (45749) 2367389
69.4%
ValueCountFrequency (%)
-9.222899436 × 101815
 
< 0.1%
-9.222232253 × 10182
 
< 0.1%
-9.220641188 × 10183
 
< 0.1%
-9.219256822 × 10184
 
< 0.1%
-9.218972161 × 10181
 
< 0.1%
-9.218357706 × 10181
 
< 0.1%
-9.218341 × 101829
 
< 0.1%
-9.218007809 × 10184
 
< 0.1%
-9.217865649 × 10181
 
< 0.1%
-9.217785961 × 1018177
< 0.1%
ValueCountFrequency (%)
9.222877123 × 1018352
< 0.1%
9.222874645 × 10181
 
< 0.1%
9.222821118 × 10185
 
< 0.1%
9.222782455 × 101823
 
< 0.1%
9.222660741 × 1018123
 
< 0.1%
9.222173967 × 10187
 
< 0.1%
9.220787458 × 10181
 
< 0.1%
9.220055689 × 10184
 
< 0.1%
9.219903649 × 10182
 
< 0.1%
9.219825771 × 101828
 
< 0.1%

merchant_mcc_code
Real number (ℝ)

High correlation 

Distinct109
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5558.5686
Minimum1711
Maximum9402
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:27.321309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1711
5-th percentile4121
Q15300
median5499
Q35812
95-th percentile7538
Maximum9402
Range7691
Interquartile range (IQR)512

Descriptive statistics

Standard deviation880.57208
Coefficient of variation (CV)0.15841706
Kurtosis3.5252759
Mean5558.5686
Median Absolute Deviation (MAD)313
Skewness1.1979504
Sum1.8956359 × 1010
Variance775407.19
MonotonicityNot monotonic
2024-12-03T07:51:27.493965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5411 406388
 
11.9%
5499 365779
 
10.7%
5541 358479
 
10.5%
5812 252661
 
7.4%
5912 197018
 
5.8%
4784 184974
 
5.4%
5300 155421
 
4.6%
4829 149676
 
4.4%
4121 138385
 
4.1%
7538 127353
 
3.7%
Other values (99) 1074161
31.5%
ValueCountFrequency (%)
1711 797
< 0.1%
3000 761
< 0.1%
3001 808
< 0.1%
3005 114
 
< 0.1%
3006 85
 
< 0.1%
3007 93
 
< 0.1%
3008 110
 
< 0.1%
3009 99
 
< 0.1%
3058 785
< 0.1%
3066 686
< 0.1%
ValueCountFrequency (%)
9402 24844
0.7%
8931 959
 
< 0.1%
8111 1783
 
0.1%
8099 3953
 
0.1%
8062 930
 
< 0.1%
8049 2676
 
0.1%
8043 4342
 
0.1%
8041 2704
 
0.1%
8021 8294
 
0.2%
8011 6307
 
0.2%

merchant_state
Text

Missing 

Distinct141
Distinct (%)< 0.1%
Missing428339
Missing (%)12.6%
Memory size26.0 MiB
2024-12-03T07:51:27.799154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length2
Mean length2.0384164
Min length2

Characters and Unicode

Total characters6078468
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowLA
2nd rowLA
3rd rowLA
4th rowLA
5th rowLA
ValueCountFrequency (%)
ca 363298
 
12.2%
tx 249893
 
8.4%
ny 199160
 
6.7%
fl 185751
 
6.2%
oh 122747
 
4.1%
il 120400
 
4.0%
pa 115015
 
3.9%
nc 104236
 
3.5%
mi 89914
 
3.0%
ga 89031
 
3.0%
Other values (147) 1345629
45.1%
2024-12-03T07:51:28.167381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 957971
15.8%
N 666398
11.0%
C 592476
 
9.7%
I 423162
 
7.0%
L 396857
 
6.5%
T 393080
 
6.5%
M 363886
 
6.0%
O 276848
 
4.6%
X 249893
 
4.1%
Y 244172
 
4.0%
Other values (42) 1513725
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6078468
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 957971
15.8%
N 666398
11.0%
C 592476
 
9.7%
I 423162
 
7.0%
L 396857
 
6.5%
T 393080
 
6.5%
M 363886
 
6.0%
O 276848
 
4.6%
X 249893
 
4.1%
Y 244172
 
4.0%
Other values (42) 1513725
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6078468
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 957971
15.8%
N 666398
11.0%
C 592476
 
9.7%
I 423162
 
7.0%
L 396857
 
6.5%
T 393080
 
6.5%
M 363886
 
6.0%
O 276848
 
4.6%
X 249893
 
4.1%
Y 244172
 
4.0%
Other values (42) 1513725
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6078468
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 957971
15.8%
N 666398
11.0%
C 592476
 
9.7%
I 423162
 
7.0%
L 396857
 
6.5%
T 393080
 
6.5%
M 363886
 
6.0%
O 276848
 
4.6%
X 249893
 
4.1%
Y 244172
 
4.0%
Other values (42) 1513725
24.9%

merchant_zip
Real number (ℝ)

High correlation  Missing 

Distinct19387
Distinct (%)0.7%
Missing449930
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean51070.572
Minimum1001
Maximum99840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.0 MiB
2024-12-03T07:51:28.322746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile7306
Q128312
median47341
Q377571
95-th percentile95621
Maximum99840
Range98839
Interquartile range (IQR)49259

Descriptive statistics

Standard deviation29393.782
Coefficient of variation (CV)0.57555224
Kurtosis-1.2754833
Mean51070.572
Median Absolute Deviation (MAD)26601
Skewness0.086279869
Sum1.5118753 × 1011
Variance8.6399443 × 108
MonotonicityNot monotonic
2024-12-03T07:51:28.489595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98516 7814
 
0.2%
75023 6786
 
0.2%
91606 6590
 
0.2%
87121 6425
 
0.2%
77056 5574
 
0.2%
94606 5546
 
0.2%
80013 5346
 
0.2%
40299 5206
 
0.2%
95687 5185
 
0.2%
29229 5110
 
0.1%
Other values (19377) 2900783
85.1%
(Missing) 449930
 
13.2%
ValueCountFrequency (%)
1001 10
< 0.1%
1002 4
 
< 0.1%
1009 1
 
< 0.1%
1010 5
< 0.1%
1011 1
 
< 0.1%
1012 12
< 0.1%
1013 12
< 0.1%
1020 3
 
< 0.1%
1022 8
< 0.1%
1027 1
 
< 0.1%
ValueCountFrequency (%)
99840 3
 
< 0.1%
99835 8
 
< 0.1%
99833 1
 
< 0.1%
99829 12
< 0.1%
99827 2
 
< 0.1%
99826 1
 
< 0.1%
99824 2
 
< 0.1%
99803 12
< 0.1%
99802 6
 
< 0.1%
99801 20
< 0.1%

transaction_amount
Real number (ℝ)

Distinct50374
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.04289
Minimum-500
Maximum4804.21
Zeros2695
Zeros (%)0.1%
Negative164835
Negative (%)4.8%
Memory size26.0 MiB
2024-12-03T07:51:28.663294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-500
5-th percentile0.13
Q19.08
median29.25
Q363.1
95-th percentile145.88
Maximum4804.21
Range5304.21
Interquartile range (IQR)54.02

Descriptive statistics

Standard deviation81.403066
Coefficient of variation (CV)1.8912082
Kurtosis95.108947
Mean43.04289
Median Absolute Deviation (MAD)24
Skewness5.0927513
Sum1.4678895 × 108
Variance6626.4591
MonotonicityNot monotonic
2024-12-03T07:51:28.872680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 33437
 
1.0%
100 31437
 
0.9%
60 25987
 
0.8%
120 19880
 
0.6%
40 14204
 
0.4%
140 11288
 
0.3%
20 8111
 
0.2%
160 5617
 
0.2%
50 3125
 
0.1%
54 3100
 
0.1%
Other values (50364) 3254109
95.4%
ValueCountFrequency (%)
-500 47
< 0.1%
-499 57
< 0.1%
-498 50
< 0.1%
-497 53
< 0.1%
-496 46
< 0.1%
-495 48
< 0.1%
-494 46
< 0.1%
-493 53
< 0.1%
-492 57
< 0.1%
-491 42
< 0.1%
ValueCountFrequency (%)
4804.21 1
< 0.1%
4645.71 1
< 0.1%
4633.43 1
< 0.1%
4077.18 1
< 0.1%
3865.86 1
< 0.1%
3752.4 1
< 0.1%
3679.33 1
< 0.1%
3677.13 1
< 0.1%
3524.62 1
< 0.1%
3523.13 1
< 0.1%
Distinct883198
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Memory size26.0 MiB
Minimum2015-01-01 00:01:00
Maximum2016-12-31 23:59:00
2024-12-03T07:51:29.023143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-03T07:51:29.190267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

transaction_error
Categorical

High correlation  Imbalance  Missing 

Distinct20
Distinct (%)< 0.1%
Missing3355771
Missing (%)98.4%
Memory size26.0 MiB
Insufficient Balance
33280 
Bad PIN
8187 
Technical Glitch
6875 
Bad Card Number
 
2134
Bad Expiration
 
1764
Other values (15)
 
2284

Length

Max length37
Median length20
Mean length16.744608
Min length7

Characters and Unicode

Total characters912983
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInsufficient Balance
2nd rowInsufficient Balance
3rd rowInsufficient Balance
4th rowInsufficient Balance
5th rowInsufficient Balance

Common Values

ValueCountFrequency (%)
Insufficient Balance 33280
 
1.0%
Bad PIN 8187
 
0.2%
Technical Glitch 6875
 
0.2%
Bad Card Number 2134
 
0.1%
Bad Expiration 1764
 
0.1%
Bad CVV 1740
 
0.1%
Bad Zipcode 282
 
< 0.1%
Bad PIN,Insufficient Balance 73
 
< 0.1%
Insufficient Balance,Technical Glitch 66
 
< 0.1%
Bad PIN,Technical Glitch 20
 
< 0.1%
Other values (10) 103
 
< 0.1%
(Missing) 3355771
98.4%

Length

2024-12-03T07:51:29.424668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
balance 33411
30.0%
insufficient 33346
29.9%
bad 14303
12.8%
pin 8187
 
7.3%
glitch 6976
 
6.3%
technical 6875
 
6.2%
card 2182
 
2.0%
number 2134
 
1.9%
expiration 1777
 
1.6%
cvv 1757
 
1.6%
Other values (13) 544
 
0.5%

Most occurring characters

ValueCountFrequency (%)
n 109213
12.0%
a 92251
10.1%
c 88166
9.7%
i 84802
9.3%
e 76396
 
8.4%
f 66954
 
7.3%
56968
 
6.2%
B 47810
 
5.2%
l 47429
 
5.2%
t 42259
 
4.6%
Other values (20) 200735
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 912983
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 109213
12.0%
a 92251
10.1%
c 88166
9.7%
i 84802
9.3%
e 76396
 
8.4%
f 66954
 
7.3%
56968
 
6.2%
B 47810
 
5.2%
l 47429
 
5.2%
t 42259
 
4.6%
Other values (20) 200735
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 912983
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 109213
12.0%
a 92251
10.1%
c 88166
9.7%
i 84802
9.3%
e 76396
 
8.4%
f 66954
 
7.3%
56968
 
6.2%
B 47810
 
5.2%
l 47429
 
5.2%
t 42259
 
4.6%
Other values (20) 200735
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 912983
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 109213
12.0%
a 92251
10.1%
c 88166
9.7%
i 84802
9.3%
e 76396
 
8.4%
f 66954
 
7.3%
56968
 
6.2%
B 47810
 
5.2%
l 47429
 
5.2%
t 42259
 
4.6%
Other values (20) 200735
22.0%

transaction_type
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.0 MiB
Chip Transaction
2401255 
Swipe Transaction
583576 
Online Transaction
425464 

Length

Max length18
Median length16
Mean length16.420639
Min length16

Characters and Unicode

Total characters55999224
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChip Transaction
2nd rowChip Transaction
3rd rowChip Transaction
4th rowChip Transaction
5th rowChip Transaction

Common Values

ValueCountFrequency (%)
Chip Transaction 2401255
70.4%
Swipe Transaction 583576
 
17.1%
Online Transaction 425464
 
12.5%

Length

2024-12-03T07:51:29.658868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T07:51:29.804623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
transaction 3410295
50.0%
chip 2401255
35.2%
swipe 583576
 
8.6%
online 425464
 
6.2%

Most occurring characters

ValueCountFrequency (%)
n 7671518
13.7%
i 6820590
12.2%
a 6820590
12.2%
s 3410295
 
6.1%
t 3410295
 
6.1%
3410295
 
6.1%
T 3410295
 
6.1%
r 3410295
 
6.1%
o 3410295
 
6.1%
c 3410295
 
6.1%
Other values (8) 10814461
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55999224
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 7671518
13.7%
i 6820590
12.2%
a 6820590
12.2%
s 3410295
 
6.1%
t 3410295
 
6.1%
3410295
 
6.1%
T 3410295
 
6.1%
r 3410295
 
6.1%
o 3410295
 
6.1%
c 3410295
 
6.1%
Other values (8) 10814461
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55999224
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 7671518
13.7%
i 6820590
12.2%
a 6820590
12.2%
s 3410295
 
6.1%
t 3410295
 
6.1%
3410295
 
6.1%
T 3410295
 
6.1%
r 3410295
 
6.1%
o 3410295
 
6.1%
c 3410295
 
6.1%
Other values (8) 10814461
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55999224
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 7671518
13.7%
i 6820590
12.2%
a 6820590
12.2%
s 3410295
 
6.1%
t 3410295
 
6.1%
3410295
 
6.1%
T 3410295
 
6.1%
r 3410295
 
6.1%
o 3410295
 
6.1%
c 3410295
 
6.1%
Other values (8) 10814461
19.3%

Interactions

Correlations

2024-12-03T07:51:29.929185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
customer_idbirth_monthbirth_yearcredit_card_countcredit_scorecurrent_agelatitudelongitudeper_capita_incomeretirement_agetotal_debtyearly_incomecard_idcard_indexcredit_limitnumber_cards_issuedpin_last_changed_yeartransaction_idtransaction_amount
customer_id1.000-0.037-0.022-0.0070.0470.0230.0420.014-0.064-0.019-0.034-0.0541.000-0.005-0.039-0.0320.0091.000-0.010
birth_month-0.0371.0000.000-0.000-0.047-0.016-0.059-0.025-0.0150.022-0.009-0.006-0.0370.010-0.0040.012-0.021-0.037-0.011
birth_year-0.0220.0001.000-0.3860.006-1.0000.0270.059-0.0190.0200.3610.110-0.023-0.2270.034-0.0230.049-0.0230.003
credit_card_count-0.007-0.000-0.3861.0000.2690.386-0.003-0.0680.0110.161-0.223-0.043-0.0070.5970.073-0.073-0.015-0.008-0.010
credit_score0.047-0.0470.0060.2691.000-0.0050.038-0.004-0.0440.160-0.128-0.0410.0470.1670.048-0.059-0.0150.047-0.012
current_age0.023-0.016-1.0000.386-0.0051.000-0.026-0.0590.019-0.021-0.361-0.1110.0230.227-0.0340.023-0.0490.023-0.003
latitude0.042-0.0590.027-0.0030.038-0.0261.0000.1390.119-0.0060.0270.1130.042-0.0190.082-0.0320.0140.0430.008
longitude0.014-0.0250.059-0.068-0.004-0.0590.1391.0000.0380.0020.0380.0640.014-0.0500.0340.0090.0330.0130.013
per_capita_income-0.064-0.015-0.0190.011-0.0440.0190.1190.0381.000-0.0530.4470.952-0.0630.0030.587-0.004-0.012-0.0640.101
retirement_age-0.0190.0220.0200.1610.160-0.021-0.0060.002-0.0531.000-0.051-0.024-0.0200.1130.012-0.080-0.003-0.021-0.007
total_debt-0.034-0.0090.361-0.223-0.128-0.3610.0270.0380.447-0.0511.0000.514-0.034-0.1230.2530.026-0.009-0.0340.048
yearly_income-0.054-0.0060.110-0.043-0.041-0.1110.1130.0640.952-0.0240.5141.000-0.054-0.0270.557-0.004-0.006-0.0550.100
card_id1.000-0.037-0.023-0.0070.0470.0230.0420.014-0.063-0.020-0.034-0.0541.000-0.004-0.039-0.0320.0091.000-0.011
card_index-0.0050.010-0.2270.5970.1670.227-0.019-0.0500.0030.113-0.123-0.027-0.0041.0000.013-0.007-0.016-0.004-0.005
credit_limit-0.039-0.0040.0340.0730.048-0.0340.0820.0340.5870.0120.2530.557-0.0390.0131.000-0.083-0.022-0.0390.063
number_cards_issued-0.0320.012-0.023-0.073-0.0590.023-0.0320.009-0.004-0.0800.026-0.004-0.032-0.007-0.0831.0000.029-0.0310.004
pin_last_changed_year0.009-0.0210.049-0.015-0.015-0.0490.0140.033-0.012-0.003-0.009-0.0060.009-0.016-0.0220.0291.0000.0080.006
transaction_id1.000-0.037-0.023-0.0080.0470.0230.0430.013-0.064-0.021-0.034-0.0551.000-0.004-0.039-0.0310.0081.000-0.011
transaction_amount-0.010-0.0110.003-0.010-0.012-0.0030.0080.0130.101-0.0070.0480.100-0.011-0.0050.0630.0040.006-0.0111.000
2024-12-03T07:51:30.420299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
customer_idbirth_monthbirth_yearcredit_card_countcredit_scorecurrent_agelatitudelongitudeper_capita_incomeretirement_agetotal_debtyearly_incomecard_idcard_indexcredit_limitnumber_cards_issuedpin_last_changed_yeartransaction_idtransaction_amount
customer_id1.000-0.037-0.028-0.0110.0430.0280.0390.017-0.068-0.021-0.030-0.0611.000-0.007-0.034-0.0320.0151.000-0.017
birth_month-0.0371.0000.005-0.005-0.046-0.021-0.051-0.0090.0090.015-0.0080.002-0.0370.0090.0150.015-0.022-0.037-0.014
birth_year-0.0280.0051.000-0.3610.002-1.0000.0120.0260.0140.0280.3880.179-0.028-0.1800.052-0.0280.058-0.0280.009
credit_card_count-0.011-0.005-0.3611.0000.2560.360-0.009-0.056-0.0000.170-0.243-0.058-0.0110.5630.067-0.069-0.017-0.011-0.027
credit_score0.043-0.0460.0020.2561.000-0.0020.043-0.012-0.0120.148-0.115-0.0060.0430.1650.067-0.067-0.0170.043-0.020
current_age0.028-0.021-1.0000.360-0.0021.000-0.012-0.027-0.014-0.029-0.387-0.1800.0280.180-0.0520.028-0.0580.028-0.009
latitude0.039-0.0510.012-0.0090.043-0.0121.0000.2410.179-0.0070.0150.1700.039-0.0160.098-0.0300.0130.0390.005
longitude0.017-0.0090.026-0.056-0.012-0.0270.2411.0000.112-0.0430.0520.1350.017-0.0350.0690.0050.0240.0170.023
per_capita_income-0.0680.0090.014-0.000-0.012-0.0140.1790.1121.000-0.0380.3240.919-0.068-0.0080.493-0.018-0.001-0.0680.115
retirement_age-0.0210.0150.0280.1700.148-0.029-0.007-0.043-0.0381.000-0.0300.001-0.0210.1300.039-0.082-0.012-0.021-0.006
total_debt-0.030-0.0080.388-0.243-0.115-0.3870.0150.0520.324-0.0301.0000.437-0.030-0.1440.1540.031-0.005-0.0300.045
yearly_income-0.0610.0020.179-0.058-0.006-0.1800.1700.1350.9190.0010.4371.000-0.061-0.0370.459-0.0200.007-0.0610.107
card_id1.000-0.037-0.028-0.0110.0430.0280.0390.017-0.068-0.021-0.030-0.0611.000-0.006-0.034-0.0320.0151.000-0.017
card_index-0.0070.009-0.1800.5630.1650.180-0.016-0.035-0.0080.130-0.144-0.037-0.0061.0000.008-0.009-0.009-0.006-0.017
credit_limit-0.0340.0150.0520.0670.067-0.0520.0980.0690.4930.0390.1540.459-0.0340.0081.000-0.104-0.021-0.0340.072
number_cards_issued-0.0320.015-0.028-0.069-0.0670.028-0.0300.005-0.018-0.0820.031-0.020-0.032-0.009-0.1041.0000.028-0.0320.008
pin_last_changed_year0.015-0.0220.058-0.017-0.017-0.0580.0130.024-0.001-0.012-0.0050.0070.015-0.009-0.0210.0281.0000.015-0.001
transaction_id1.000-0.037-0.028-0.0110.0430.0280.0390.017-0.068-0.021-0.030-0.0611.000-0.006-0.034-0.0320.0151.000-0.017
transaction_amount-0.017-0.0140.009-0.027-0.020-0.0090.0050.0230.115-0.0060.0450.107-0.017-0.0170.0720.008-0.001-0.0171.000
2024-12-03T07:51:30.831065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
customer_idbirth_monthbirth_yearcredit_card_countcredit_scorecurrent_agelatitudelongitudeper_capita_incomeretirement_agetotal_debtyearly_incomecard_idcard_indexcredit_limitnumber_cards_issuedpin_last_changed_yeartransaction_idtransaction_amount
customer_id1.000-0.026-0.019-0.0080.0280.0200.0260.011-0.045-0.015-0.020-0.0411.000-0.005-0.023-0.0260.0111.000-0.011
birth_month-0.0261.0000.003-0.003-0.032-0.014-0.035-0.0060.0060.011-0.0060.001-0.0260.0070.0110.012-0.016-0.026-0.010
birth_year-0.0190.0031.000-0.2690.002-0.9950.0080.0170.0100.0200.2730.121-0.019-0.1360.035-0.0230.042-0.0190.006
credit_card_count-0.008-0.003-0.2691.0000.1870.268-0.007-0.041-0.0000.128-0.179-0.042-0.0080.4890.049-0.060-0.013-0.008-0.020
credit_score0.028-0.0320.0020.1871.000-0.0020.029-0.007-0.0080.102-0.078-0.0040.0280.1230.044-0.054-0.0120.028-0.013
current_age0.020-0.014-0.9950.268-0.0021.000-0.007-0.018-0.010-0.021-0.272-0.1220.0200.135-0.0360.023-0.0420.020-0.006
latitude0.026-0.0350.008-0.0070.029-0.0071.0000.1600.119-0.0050.0090.1120.026-0.0120.066-0.0250.0090.0260.003
longitude0.011-0.0060.017-0.041-0.007-0.0180.1601.0000.078-0.0300.0360.0940.011-0.0260.0470.0040.0170.0110.015
per_capita_income-0.0450.0060.010-0.000-0.008-0.0100.1190.0781.000-0.0260.2310.850-0.045-0.0060.352-0.014-0.001-0.0450.077
retirement_age-0.0150.0110.0200.1280.102-0.021-0.005-0.030-0.0261.000-0.0210.002-0.0150.1010.027-0.070-0.008-0.015-0.004
total_debt-0.020-0.0060.273-0.179-0.078-0.2720.0090.0360.231-0.0211.0000.319-0.020-0.1080.1050.026-0.003-0.0200.030
yearly_income-0.0410.0010.121-0.042-0.004-0.1220.1120.0940.8500.0020.3191.000-0.041-0.0280.326-0.0170.005-0.0410.072
card_id1.000-0.026-0.019-0.0080.0280.0200.0260.011-0.045-0.015-0.020-0.0411.000-0.004-0.023-0.0260.0111.000-0.011
card_index-0.0050.007-0.1360.4890.1230.135-0.012-0.026-0.0060.101-0.108-0.028-0.0041.0000.006-0.008-0.007-0.004-0.012
credit_limit-0.0230.0110.0350.0490.044-0.0360.0660.0470.3520.0270.1050.326-0.0230.0061.000-0.085-0.015-0.0230.048
number_cards_issued-0.0260.012-0.023-0.060-0.0540.023-0.0250.004-0.014-0.0700.026-0.017-0.026-0.008-0.0851.0000.024-0.0260.007
pin_last_changed_year0.011-0.0160.042-0.013-0.012-0.0420.0090.017-0.001-0.008-0.0030.0050.011-0.007-0.0150.0241.0000.011-0.001
transaction_id1.000-0.026-0.019-0.0080.0280.0200.0260.011-0.045-0.015-0.020-0.0411.000-0.004-0.023-0.0260.0111.000-0.011
transaction_amount-0.011-0.0100.006-0.020-0.013-0.0060.0030.0150.077-0.0040.0300.072-0.011-0.0120.0480.007-0.001-0.0111.000
2024-12-03T07:51:31.169065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
customer_idbirth_monthbirth_yearcredit_card_countcredit_scorecurrent_agegenderlatitudelongitudeper_capita_incomeretirement_agetotal_debtyearly_incomecard_idcard_brandcard_indexcard_numbercard_typecredit_limitcvv_codehas_chipnumber_cards_issuedpin_last_changed_yeartransaction_idfraud_detectedmerchant_idmerchant_mcc_codemerchant_ziptransaction_amounttransaction_errortransaction_type
customer_id1.0000.2590.2590.1790.2860.2610.1290.1770.1650.2080.2700.1960.2940.9990.0980.0890.1060.0930.1430.1700.0620.1090.2080.9990.0030.0830.0450.2310.0290.0460.056
birth_month0.2591.0000.2570.1760.2970.2440.0930.1710.1790.2070.2810.2080.2890.2540.1350.0770.1380.0930.1330.2120.0470.0940.2050.2640.0070.0960.0600.2540.0380.0500.067
birth_year0.2590.2571.0000.3950.2820.9990.1160.2540.2670.1950.2430.3830.3580.2580.1080.2580.1050.0630.1200.1950.0600.0990.2560.2590.0130.0980.0760.2720.0420.0700.074
credit_card_count0.1790.1760.3951.0000.3030.3740.1050.1770.2770.1940.2730.3470.1820.1820.0960.7930.1010.1080.1940.1390.0680.1650.1070.1850.0160.0550.0900.1670.0330.0520.092
credit_score0.2860.2970.2820.3031.0000.2580.0730.1860.1800.2430.3910.2810.3640.2710.1210.1750.1280.0870.1280.1890.0760.1460.2120.2770.0070.0860.0650.2400.0390.1210.091
current_age0.2610.2440.9990.3740.2581.0000.1270.2230.2300.1920.2360.3850.3640.2600.1080.2330.1030.0560.1220.1970.0570.1010.2560.2590.0130.0920.0770.2850.0330.0680.067
gender0.1290.0930.1160.1050.0730.1271.0000.0670.0570.1020.0750.1180.0840.1500.0390.0520.0550.0120.0370.0900.0270.0180.0940.1780.0000.0430.0160.0510.0020.0230.013
latitude0.1770.1710.2540.1770.1860.2230.0671.0000.8360.3320.1490.1490.2100.1760.0730.1190.0810.0400.1250.1340.0620.0640.1290.1750.0060.0660.0540.6850.0160.0630.066
longitude0.1650.1790.2670.2770.1800.2300.0570.8361.0000.2460.2410.2250.2580.1670.0670.1830.0900.0940.2170.1180.0650.1150.1130.1600.0070.1070.0810.8350.0320.0550.115
per_capita_income0.2080.2070.1950.1940.2430.1920.1020.3320.2461.0000.2320.5870.9430.2050.1090.0860.1020.0740.6670.1230.0530.0460.1090.2070.0050.0650.0770.2410.0680.0970.081
retirement_age0.2700.2810.2430.2730.3910.2360.0750.1490.2410.2321.0000.2480.2960.2690.1130.1520.1170.1040.1940.1710.0550.2110.1930.2670.0060.0840.0590.2660.0240.0460.061
total_debt0.1960.2080.3830.3470.2810.3850.1180.1490.2250.5870.2481.0000.6650.1900.1080.2090.1340.1310.4880.1510.1040.1500.1660.1910.0070.0690.0740.1870.0340.0700.110
yearly_income0.2940.2890.3580.1820.3640.3640.0840.2100.2580.9430.2960.6651.0000.2920.1040.1000.1220.0880.6700.1870.0700.0780.1810.2930.0080.0880.0630.3370.0900.1260.084
card_id0.9990.2540.2580.1820.2710.2600.1500.1760.1670.2050.2690.1900.2921.0000.0980.0890.1020.0860.1380.1710.0640.1120.2111.0000.0030.0870.0510.2390.0340.0530.063
card_brand0.0980.1350.1080.0960.1210.1080.0390.0730.0670.1090.1130.1080.1040.0981.0000.0820.9910.3630.1430.0910.0540.0270.1060.0980.0030.0290.0280.0730.0530.1480.018
card_index0.0890.0770.2580.7930.1750.2330.0520.1190.1830.0860.1520.2090.1000.0890.0821.0000.0920.1080.1790.0970.0470.0800.1090.0940.0100.0310.0500.0830.0190.0150.067
card_number0.1060.1380.1050.1010.1280.1030.0550.0810.0900.1020.1170.1340.1220.1020.9910.0921.0000.6390.1450.1080.0570.0900.1210.1040.0030.0320.0310.0910.0470.1110.051
card_type0.0930.0930.0630.1080.0870.0560.0120.0400.0940.0740.1040.1310.0880.0860.3630.1080.6391.0000.5650.0970.0100.0750.0980.0810.0040.0410.1280.0670.1440.3190.040
credit_limit0.1430.1330.1200.1940.1280.1220.0370.1250.2170.6670.1940.4880.6700.1380.1430.1790.1450.5651.0000.1370.0570.1590.1310.1310.0050.0410.0650.1600.0340.0890.077
cvv_code0.1700.2120.1950.1390.1890.1970.0900.1340.1180.1230.1710.1510.1870.1710.0910.0970.1080.0970.1371.0000.0570.1250.2110.1800.0060.0680.0280.1650.0210.0280.053
has_chip0.0620.0470.0600.0680.0760.0570.0270.0620.0650.0530.0550.1040.0700.0640.0540.0470.0570.0100.0570.0571.0000.0160.0640.0680.0040.0350.0210.0800.0180.0000.409
number_cards_issued0.1090.0940.0990.1650.1460.1010.0180.0640.1150.0460.2110.1500.0780.1120.0270.0800.0900.0750.1590.1250.0161.0000.1440.1080.0000.0330.0320.0950.0180.0000.045
pin_last_changed_year0.2080.2050.2560.1070.2120.2560.0940.1290.1130.1090.1930.1660.1810.2110.1060.1090.1210.0980.1310.2110.0640.1441.0000.2090.0060.0600.0400.1650.0270.0510.052
transaction_id0.9990.2640.2590.1850.2770.2590.1780.1750.1600.2070.2670.1910.2931.0000.0980.0940.1040.0810.1310.1800.0680.1080.2091.0000.0040.0860.0510.2330.0340.0570.067
fraud_detected0.0030.0070.0130.0160.0070.0130.0000.0060.0070.0050.0060.0070.0080.0030.0030.0100.0030.0040.0050.0060.0040.0000.0060.0041.0000.0130.0380.0040.0430.1680.060
merchant_id0.0830.0960.0980.0550.0860.0920.0430.0660.1070.0650.0840.0690.0880.0870.0290.0310.0320.0410.0410.0680.0350.0330.0600.0860.0131.0000.4100.1170.1550.3420.398
merchant_mcc_code0.0450.0600.0760.0900.0650.0770.0160.0540.0810.0770.0590.0740.0630.0510.0280.0500.0310.1280.0650.0280.0210.0320.0400.0510.0380.4101.0000.0730.4030.4400.747
merchant_zip0.2310.2540.2720.1670.2400.2850.0510.6850.8350.2410.2660.1870.3370.2390.0730.0830.0910.0670.1600.1650.0800.0950.1650.2330.0040.1170.0731.0000.0520.0280.057
transaction_amount0.0290.0380.0420.0330.0390.0330.0020.0160.0320.0680.0240.0340.0900.0340.0530.0190.0470.1440.0340.0210.0180.0180.0270.0340.0430.1550.4030.0521.0000.3320.089
transaction_error0.0460.0500.0700.0520.1210.0680.0230.0630.0550.0970.0460.0700.1260.0530.1480.0150.1110.3190.0890.0280.0000.0000.0510.0570.1680.3420.4400.0280.3321.0000.721
transaction_type0.0560.0670.0740.0920.0910.0670.0130.0660.1150.0810.0610.1100.0840.0630.0180.0670.0510.0400.0770.0530.4090.0450.0520.0670.0600.3980.7470.0570.0890.7211.000
2024-12-03T07:51:31.562636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
birth_monthbirth_yearcard_brandcard_idcard_indexcard_numbercard_typecredit_card_countcredit_limitcredit_scorecurrent_agecustomer_idcvv_codefraud_detectedgenderhas_chiplatitudelongitudemerchant_idmerchant_mcc_codemerchant_zipnumber_cards_issuedper_capita_incomepin_last_changed_yearretirement_agetotal_debttransaction_amounttransaction_errortransaction_idtransaction_typeyearly_income
birth_month1.0000.0050.081-0.0370.0090.0110.055-0.0050.015-0.046-0.021-0.037-0.0070.0060.0710.036-0.051-0.009-0.012-0.0170.0090.0560.009-0.0220.015-0.008-0.0140.016-0.0370.0390.002
birth_year0.0051.0000.065-0.028-0.1800.0040.034-0.3610.0520.002-1.000-0.028-0.0310.0100.0880.0470.0120.026-0.002-0.003-0.0220.0590.0140.0580.0280.3880.0090.022-0.0280.0420.179
card_brand0.0810.0651.0000.0590.0520.9990.3520.0620.0920.0720.0650.0590.5640.0020.0260.0360.0330.0430.3380.0480.5980.0250.0490.0640.0680.0690.0320.0710.0590.0170.062
card_id-0.037-0.0280.0591.000-0.0060.0030.051-0.011-0.0340.0430.0281.000-0.0280.0020.1150.0490.0390.0170.0160.014-0.0280.067-0.0680.015-0.021-0.030-0.0170.0171.0000.038-0.061
card_index0.009-0.1800.052-0.0061.000-0.0370.0470.5630.0080.1650.180-0.0070.0020.0100.0520.047-0.016-0.0350.0160.0220.0350.035-0.008-0.0090.130-0.144-0.0170.006-0.0060.029-0.037
card_number0.0110.0040.9990.003-0.0371.0000.999-0.0230.0110.014-0.0040.0030.0010.1210.9990.999-0.004-0.015-0.0050.0050.0140.999-0.0410.037-0.040-0.021-0.0290.1010.0030.562-0.035
card_type0.0550.0340.3520.0510.0470.9991.0000.0470.2990.0510.0330.0550.5620.0070.0200.0160.0250.0420.3340.0940.5880.0220.0460.0580.0620.0580.0860.1770.0480.0120.052
credit_card_count-0.005-0.3610.062-0.0110.563-0.0230.0471.0000.0670.2560.360-0.0110.0240.0160.1050.068-0.009-0.0560.0320.0240.0570.073-0.000-0.0170.170-0.243-0.0270.021-0.0110.040-0.058
credit_limit0.0150.0520.092-0.0340.0080.0110.2990.0671.0000.067-0.052-0.0340.0250.0050.0370.0570.0980.069-0.006-0.014-0.0570.0700.493-0.0210.0390.1540.0720.035-0.0340.0340.459
credit_score-0.0460.0020.0720.0430.1650.0140.0510.2560.0671.000-0.0020.043-0.0040.0050.0570.0590.043-0.0120.0040.0190.0070.087-0.012-0.0170.148-0.115-0.0200.0380.0430.054-0.006
current_age-0.021-1.0000.0650.0280.180-0.0040.0330.360-0.052-0.0021.0000.0280.0300.0100.0970.044-0.012-0.0270.0020.0030.0230.060-0.014-0.058-0.029-0.387-0.0090.0220.0280.040-0.180
customer_id-0.037-0.0280.0591.000-0.0070.0030.055-0.011-0.0340.0430.0281.000-0.0280.0020.0990.0470.0390.0170.0160.014-0.0280.065-0.0680.015-0.021-0.030-0.0170.0141.0000.033-0.061
cvv_code-0.007-0.0310.564-0.0280.0020.0010.5620.0240.025-0.0040.030-0.0281.0000.0500.5680.5660.013-0.0000.0170.0100.0040.5800.001-0.006-0.024-0.009-0.0020.068-0.0280.327-0.005
fraud_detected0.0060.0100.0020.0020.0100.1210.0070.0160.0050.0050.0100.0020.0501.0000.0000.0020.0040.0070.3150.1800.5070.0010.0040.0050.0050.0070.0330.1320.0030.0990.006
gender0.0710.0880.0260.1150.0520.9990.0200.1050.0370.0570.0970.0990.5680.0001.0000.0170.0500.0580.4530.0450.8470.0300.0770.0720.0570.1180.0010.0190.1360.0210.064
has_chip0.0360.0470.0360.0490.0470.9990.0160.0680.0570.0590.0440.0470.5660.0020.0171.0000.0460.0650.3370.0390.6180.0260.0400.0490.0420.1030.0140.0000.0520.6420.053
latitude-0.0510.0120.0330.039-0.016-0.0040.025-0.0090.0980.043-0.0120.0390.0130.0040.0500.0461.0000.2410.0210.010-0.2020.0400.1790.013-0.0070.0150.0050.0250.0390.0420.170
longitude-0.0090.0260.0430.017-0.035-0.0150.042-0.0560.069-0.012-0.0270.017-0.0000.0070.0580.0650.2411.0000.012-0.006-0.9030.0500.1120.024-0.0430.0520.0230.0220.0170.0510.135
merchant_id-0.012-0.0020.3380.0160.016-0.0050.3340.032-0.0060.0040.0020.0160.0170.3150.4530.3370.0210.0121.0000.040-0.0070.320-0.0110.004-0.0000.004-0.0210.0000.0160.761-0.005
merchant_mcc_code-0.017-0.0030.0480.0140.0220.0050.0940.024-0.0140.0190.0030.0140.0100.1800.0450.0390.010-0.0060.0401.000-0.0230.031-0.0270.0010.017-0.010-0.1350.1660.0140.599-0.017
merchant_zip0.009-0.0220.598-0.0280.0350.0140.5880.057-0.0570.0070.023-0.0280.0040.5070.8470.618-0.202-0.903-0.007-0.0231.0000.587-0.083-0.0190.027-0.041-0.0160.084-0.0280.566-0.100
number_cards_issued0.0560.0590.0250.0670.0350.9990.0220.0730.0700.0870.0600.0650.5800.0010.0300.0260.0400.0500.3200.0310.5871.0000.0290.0860.1280.0660.0110.0000.0640.0130.046
per_capita_income0.0090.0140.049-0.068-0.008-0.0410.046-0.0000.493-0.012-0.014-0.0680.0010.0040.0770.0400.1790.112-0.011-0.027-0.0830.0291.000-0.001-0.0380.3240.1150.039-0.0680.0510.919
pin_last_changed_year-0.0220.0580.0640.015-0.0090.0370.058-0.017-0.021-0.017-0.0580.015-0.0060.0050.0720.0490.0130.0240.0040.001-0.0190.086-0.0011.000-0.012-0.005-0.0010.0160.0150.0310.007
retirement_age0.0150.0280.068-0.0210.130-0.0400.0620.1700.0390.148-0.029-0.021-0.0240.0050.0570.042-0.007-0.043-0.0000.0170.0270.128-0.038-0.0121.000-0.030-0.0060.015-0.0210.0360.001
total_debt-0.0080.3880.069-0.030-0.144-0.0210.058-0.2430.154-0.115-0.387-0.030-0.0090.0070.1180.1030.0150.0520.004-0.010-0.0410.0660.324-0.005-0.0301.0000.0450.028-0.0300.0480.437
transaction_amount-0.0140.0090.032-0.017-0.017-0.0290.086-0.0270.072-0.020-0.009-0.017-0.0020.0330.0010.0140.0050.023-0.021-0.135-0.0160.0110.115-0.001-0.0060.0451.0000.161-0.0170.0530.107
transaction_error0.0160.0220.0710.0170.0060.1010.1770.0210.0350.0380.0220.0140.0680.1320.0190.0000.0250.0220.0000.1660.0840.0000.0390.0160.0150.0280.1611.0000.0180.5190.040
transaction_id-0.037-0.0280.0591.000-0.0060.0030.048-0.011-0.0340.0430.0281.000-0.0280.0030.1360.0520.0390.0170.0160.014-0.0280.064-0.0680.015-0.021-0.030-0.0170.0181.0000.040-0.061
transaction_type0.0390.0420.0170.0380.0290.5620.0120.0400.0340.0540.0400.0330.3270.0990.0210.6420.0420.0510.7610.5990.5660.0130.0510.0310.0360.0480.0530.5190.0401.0000.050
yearly_income0.0020.1790.062-0.061-0.037-0.0350.052-0.0580.459-0.006-0.180-0.061-0.0050.0060.0640.0530.1700.135-0.005-0.017-0.1000.0460.9190.0070.0010.4370.1070.040-0.0610.0501.000

Missing values

2024-12-03T07:50:04.230418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-03T07:50:20.055466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-03T07:50:57.993496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

customer_idaddressbirth_monthbirth_yearcredit_card_countcredit_scorecurrent_ageemailfirst_namegenderlast_namelatitudelongitudeper_capita_incomeretirement_agetotal_debtyearly_incomecard_idaccount_open_datecard_brandcard_expiration_datecard_indexcard_numbercard_on_dark_webcard_typecredit_limitcvv_codehas_chipnumber_cards_issuedpin_last_changed_yeartransaction_idfraud_detectedmerchant_citymerchant_idmerchant_mcc_codemerchant_statemerchant_ziptransaction_amounttransaction_datetimetransaction_errortransaction_type
01990295 West Avenue, Bienville, LA 7100821953681167jaziel.howard@example.comJazielMaleHoward32.36-92.9716760.06971366.034172.061172009-04-01Mastercard2024-03-0125692198759214977FalseCredit11400.0761True2201048682211FalseCoushatta-24724817393551115877538LA7101950.762016-06-21 13:19:00NoneChip Transaction
11990295 West Avenue, Bienville, LA 7100821953681167jaziel.howard@example.comJazielMaleHoward32.36-92.9716760.06971366.034172.061172009-04-01Mastercard2024-03-0125692198759214977FalseCredit11400.0761True2201048682212FalseBlanchard61352085689234494089402LA7100923.142016-06-22 08:47:00NoneChip Transaction
21990295 West Avenue, Bienville, LA 7100821953681167jaziel.howard@example.comJazielMaleHoward32.36-92.9716760.06971366.034172.061172009-04-01Mastercard2024-03-0125692198759214977FalseCredit11400.0761True2201048682213FalseBienville76679580228346967875411LA7100895.632016-06-25 11:05:00NoneChip Transaction
31990295 West Avenue, Bienville, LA 7100821953681167jaziel.howard@example.comJazielMaleHoward32.36-92.9716760.06971366.034172.061172009-04-01Mastercard2024-03-0125692198759214977FalseCredit11400.0761True2201048682214FalseCotton Valley-73711132058016135035812LA7101838.842016-06-25 18:36:00NoneChip Transaction
41990295 West Avenue, Bienville, LA 7100821953681167jaziel.howard@example.comJazielMaleHoward32.36-92.9716760.06971366.034172.061172009-04-01Mastercard2024-03-0125692198759214977FalseCredit11400.0761True2201048682215FalseCoushatta-87334028873667597645814LA7101964.192016-06-28 18:31:00NoneChip Transaction
51990295 West Avenue, Bienville, LA 7100821953681167jaziel.howard@example.comJazielMaleHoward32.36-92.9716760.06971366.034172.061172009-04-01Mastercard2024-03-0125692198759214977FalseCredit11400.0761True2201048682216FalseCoushatta-87334028873667597645814LA7101976.102016-07-06 18:54:00NoneChip Transaction
61990295 West Avenue, Bienville, LA 7100821953681167jaziel.howard@example.comJazielMaleHoward32.36-92.9716760.06971366.034172.061172009-04-01Mastercard2024-03-0125692198759214977FalseCredit11400.0761True2201048682217FalseCoushatta-87334028873667597645814LA7101979.372016-07-08 18:53:00NoneChip Transaction
71990295 West Avenue, Bienville, LA 7100821953681167jaziel.howard@example.comJazielMaleHoward32.36-92.9716760.06971366.034172.061172009-04-01Mastercard2024-03-0125692198759214977FalseCredit11400.0761True2201048682218FalseMansfield-67610661960298414435310LA7105252.692016-07-12 07:06:00NoneChip Transaction
81990295 West Avenue, Bienville, LA 7100821953681167jaziel.howard@example.comJazielMaleHoward32.36-92.9716760.06971366.034172.061172009-04-01Mastercard2024-03-0125692198759214977FalseCredit11400.0761True2201048682219FalseCoushatta19134774605907658605300LA710191.762016-07-12 11:12:00NoneChip Transaction
91990295 West Avenue, Bienville, LA 7100821953681167jaziel.howard@example.comJazielMaleHoward32.36-92.9716760.06971366.034172.061172009-04-01Mastercard2024-03-0125692198759214977FalseCredit11400.0761True2201048682220FalseBienville-11396817375977742177230LA7100892.852016-07-13 16:07:00NoneChip Transaction
customer_idaddressbirth_monthbirth_yearcredit_card_countcredit_scorecurrent_ageemailfirst_namegenderlast_namelatitudelongitudeper_capita_incomeretirement_agetotal_debtyearly_incomecard_idaccount_open_datecard_brandcard_expiration_datecard_indexcard_numbercard_on_dark_webcard_typecredit_limitcvv_codehas_chipnumber_cards_issuedpin_last_changed_yeartransaction_idfraud_detectedmerchant_citymerchant_idmerchant_mcc_codemerchant_statemerchant_ziptransaction_amounttransaction_datetimetransaction_errortransaction_type
34102851990295 West Avenue, Bienville, LA 7100821953681167jaziel.howard@example.comJazielMaleHoward32.36-92.9716760.06971366.034172.061172009-04-01Mastercard2024-03-0125692198759214977FalseCredit11400.0761True2201048682201FalseCoushatta-87334028873667597645814LA7101945.192016-06-12 18:31:00NoneChip Transaction
34102861990295 West Avenue, Bienville, LA 7100821953681167jaziel.howard@example.comJazielMaleHoward32.36-92.9716760.06971366.034172.061172009-04-01Mastercard2024-03-0125692198759214977FalseCredit11400.0761True2201048682202FalseCotton Valley-73711132058016135035812LA7101859.902016-06-12 19:41:00NoneChip Transaction
34102871990295 West Avenue, Bienville, LA 7100821953681167jaziel.howard@example.comJazielMaleHoward32.36-92.9716760.06971366.034172.061172009-04-01Mastercard2024-03-0125692198759214977FalseCredit11400.0761True2201048682203FalseCastor-87502476146028932114900LA7101638.982016-06-14 11:53:00NoneChip Transaction
34102881990295 West Avenue, Bienville, LA 7100821953681167jaziel.howard@example.comJazielMaleHoward32.36-92.9716760.06971366.034172.061172009-04-01Mastercard2024-03-0125692198759214977FalseCredit11400.0761True2201048682204FalseONLINE-74210933786275440995311NoneNone17.992016-06-14 15:43:00NoneOnline Transaction
34102891990295 West Avenue, Bienville, LA 7100821953681167jaziel.howard@example.comJazielMaleHoward32.36-92.9716760.06971366.034172.061172009-04-01Mastercard2024-03-0125692198759214977FalseCredit11400.0761True2201048682205FalseGrand Cane-7276120921399160435411LA7103275.222016-06-16 11:06:00NoneChip Transaction
34102901990295 West Avenue, Bienville, LA 7100821953681167jaziel.howard@example.comJazielMaleHoward32.36-92.9716760.06971366.034172.061172009-04-01Mastercard2024-03-0125692198759214977FalseCredit11400.0761True2201048682206FalseBlanchard41683387566908009475813LA7100922.042016-06-17 22:25:00NoneChip Transaction
34102911990295 West Avenue, Bienville, LA 7100821953681167jaziel.howard@example.comJazielMaleHoward32.36-92.9716760.06971366.034172.061172009-04-01Mastercard2024-03-0125692198759214977FalseCredit11400.0761True2201048682207FalseONLINE70356025694091498345311NoneNone2.202016-06-18 11:26:00NoneOnline Transaction
34102921990295 West Avenue, Bienville, LA 7100821953681167jaziel.howard@example.comJazielMaleHoward32.36-92.9716760.06971366.034172.061172009-04-01Mastercard2024-03-0125692198759214977FalseCredit11400.0761True2201048682208FalseBienville76679580228346967875411LA7100871.492016-06-19 11:03:00NoneChip Transaction
34102931990295 West Avenue, Bienville, LA 7100821953681167jaziel.howard@example.comJazielMaleHoward32.36-92.9716760.06971366.034172.061172009-04-01Mastercard2024-03-0125692198759214977FalseCredit11400.0761True2201048682209FalseCotton Valley-73711132058016135035812LA7101854.372016-06-19 18:47:00NoneChip Transaction
34102941990295 West Avenue, Bienville, LA 7100821953681167jaziel.howard@example.comJazielMaleHoward32.36-92.9716760.06971366.034172.061172009-04-01Mastercard2024-03-0125692198759214977FalseCredit11400.0761True2201048682210FalseBastrop-71466707481252008985970LA7122044.392016-06-20 07:25:00NoneSwipe Transaction